MO265LONGITUDINAL CHANGE IN PROTEINURIA AND KIDNEY OUTCOMES IN C3 GLOMERULOPATHY

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
Fernando Caravaca-Fontán ◽  
Elena Goicoechea de Jorge ◽  
Manuel Praga
Keyword(s):  
Kidney Failure ◽  
Cox Model ◽  
Strong Association ◽  
Fold Increase ◽  
Joint Modeling ◽  
Longitudinal Change ◽  
C3 Glomerulopathy ◽  
Dynamic Prediction ◽  
Over Time

Abstract Background and Aims The association between a change in proteinuria over time and its impact in kidney prognosis has not been analyzed in C3 glomerulopathy. This study aims to investigate the association between the longitudinal change in proteinuria and the risk of kidney failure. Method Retrospective, multicenter observational cohort study in 35 nephrology departments belonging to the GLOSEN group. Patients diagnosed with C3 glomerulopathy between 1995 and 2020 were enrolled. A joint modeling of linear mixed-effects models was applied to assess the underlying trajectory of a repeatedly measured proteinuria, and a Cox model to evaluate the association of this trajectory with the risk of kidney failure. Results The study group consisted of 85 patients, 70 C3 glomerulonephritis and 15 dense deposit disease, with a median age of 26 years (range 13–41). During a median follow-up of 42 months, 25 patients reached kidney failure. The longitudinal change in proteinuria showed a strong association with the risk of this outcome, with a doubling of proteinuria levels resulting in a 2.5-fold increase of the risk. A second model showed that a ≥50% proteinuria reduction over time was significantly associated with a lower risk of kidney failure (HR: 0.79; 95%CI:0.56–0.97; p<0.001). This association was also found when the ≥50% proteinuria reduction was observed within the first 6 and 12 months of follow-up. Conclusion The longitudinal change in proteinuria is strongly associated with the risk of kidney failure. The change in proteinuria over time can provide clinicians a dynamic prediction of kidney outcomes.

Longitudinal change in proteinuria and kidney outcomes in C3 glomerulopathy

2021 ◽  
Author(s):  
Fernando Caravaca-Fontán ◽  
Montserrat Díaz-Encarnación ◽  
Virginia Cabello ◽  
Gema Ariceta ◽  
Luis F Quintana ◽  
...  
Keyword(s):  
Kidney Failure ◽  
Cox Model ◽  
Strong Association ◽  
Fold Increase ◽  
Longitudinal Change ◽  
C3 Glomerulopathy ◽  
Dynamic Prediction ◽  
Glomerular Diseases ◽  
Over Time

Abstract Introduction The association between a change in proteinuria over time and its impact on kidney prognosis has not been analysed in complement component 3 (C3) glomerulopathy. This study aims to investigate the association between the longitudinal change in proteinuria and the risk of kidney failure. Methods This was a retrospective, multicentre observational cohort study in 35 nephrology departments belonging to the Spanish Group for the Study of Glomerular Diseases. Patients diagnosed with C3 glomerulopathy between 1995 and 2020 were enrolled. A joint modelling of linear mixed-effects models was applied to assess the underlying trajectory of a repeatedly measured proteinuria, and a Cox model to evaluate the association of this trajectory with the risk of kidney failure. Results The study group consisted of 85 patients, 70 C3 glomerulonephritis and 15 dense deposit disease, with a median age of 26 years (range 13–41). During a median follow-up of 42 months, 25 patients reached kidney failure. The longitudinal change in proteinuria showed a strong association with the risk of this outcome, with a doubling of proteinuria levels resulting in a 2.5-fold increase of the risk. A second model showed that a ≥50% proteinuria reduction over time was significantly associated with a lower risk of kidney failure (hazard ratio 0.79; 95% confidence interval 0.56–0.97; P < 0.001). This association was also found when the ≥50% proteinuria reduction was observed within the first 6 and 12 months of follow-up. Conclusions The longitudinal change in proteinuria is strongly associated with the risk of kidney failure. The change in proteinuria over time can provide clinicians a dynamic prediction of kidney outcomes.

scholarly journals C3 Glomerulopathy and Related Disorders in Children

2021 ◽  
pp. CJN.00320121
Author(s):  
Edwin Wong ◽  
Kevin Marchbank ◽  
Hannah Lomax-Browne ◽  
Isabel Pappworth ◽  
Harriet Denton ◽  
...  
Keyword(s):  
Risk Factor ◽  
Immune Complex ◽  
Kidney Failure ◽  
Alternative Pathway ◽  
Multivariable Analysis ◽  
Complement C3 ◽  
C3 Glomerulopathy ◽  
Prognostic Groups ◽  
Initial Biopsy

Background and objectives: Membranoproliferative Glomerulonephritis (MPGN) and C3 Glomerulopathy are rare and overlapping disorders associated with dysregulation of the alternative complement pathway. Specific aetiological data for paediatric MPGN/C3 glomerulopathy are lacking, and outcome data are based upon retrospective studies without aetiological data. Design, setting, participants, and measurements: Eighty prevalent pediatric patients with MPGN/C3 glomerulopathy underwent detailed phenotyping and long-term follow-up within the National Registry of Rare Kidney Diseases (RaDaR). Risk factors for kidney survival were determined using COX proportional hazards model. Kidney and transplant graft survival was determined using Kaplan-Meier method. Results: Central histology review determined 39 C3 glomerulopathy, 31 immune-complex MPGN and 10 immune-complex glomerulonephritis (GN) cases. Patients were aged 2-15 (median 9 (IQR 7-11) years. Median complement C3 and C4 levels were 0.31g/L and 0.14g/L respectively; acquired (anti-complement autoantibodies) or genetic alternative pathway abnormalities were detected in 46% and 9% patients respectively, across all groups including immune-complex GN. Median follow-up was 5.18 (IQR 2.13-8.08) years. Eleven patients (14%) progressed to kidney failure with 9 transplants performed in 8 patients, 2 of which failed due to recurrent disease. Presence of >50% crescents on initial biopsy was the sole variable associated with kidney failure in multivariable analysis (Hazard Ratio 6.2, p = 0.045; 95% CI 1.05 to 36.6). Three distinct C3 glomerulopathy prognostic groups were identified according to presenting eGFR and >50% crescents on initial biopsy. Conclusions: Crescentic disease was a key risk factor associated with kidney failure in a national cohort of pediatric MPGN/C3 glomerulopathy and immune-complex GN. Presenting eGFR and crescentic disease help define prognostic groups in pediatric C3 glomerulopathy. Acquired abnormalities of the alternative pathway were commonly identified but not a risk factor for kidney failure.

scholarly journals Association of Pulmonary Function Decline over Time with Longitudinal Change of Glycated Hemoglobin in Participants without Diabetes Mellitus

2021 ◽  
Vol 11 (10) ◽  
pp. 994
Author(s):  
Wen-Hsien Lee ◽  
Da-Wei Wu ◽  
Ying-Chih Chen ◽  
Yi-Hsueh Liu ◽  
Wei-Sheng Liao ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Lung Function ◽  
Pulmonary Function ◽  
Glycated Hemoglobin ◽  
Longitudinal Change ◽  
Type 2 Dm ◽  
Over Time ◽  
New Onset

Pulmonary damage and function impairment were frequently noted in patients with diabetes mellitus (DM). However, the relationship between lung function and glycemic status in non-DM subjects was not well-known. Here, we evaluated the association of longitudinal changes of lung function parameters with longitudinal changes of glycated hemoglobin (HbA1c) in non-DM participants. The study enrolled participants without prior type 2 DM, hypertension, and chronic obstructive pulmonary disease (COPD) from the Taiwan Biobank database. Laboratory profiles and pulmonary function parameters, including forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV1), were examined at baseline and follow-up. Finally, 7055 participants were selected in this study. During a mean 3.9-year follow-up, FVC and FEV1 were significantly decreased over time (both p < 0.001). In the multivariable analysis, the baseline (unstandardized coefficient β = −0.032, p < 0.001) and longitudinal change (unstandardized coefficient β = −0.025, p = 0.026) of FVC were negatively associated with the baseline and longitudinal change of HbA1c, respectively. Additionally, the longitudinal change of FVC was negatively associated with the risk of newly diagnosed type 2 DM (p = 0.018). During a mean 3.9-year follow-up, our present study, including participants without type 2 DM, hypertension, and COPD, demonstrated that the baseline and longitudinal change of FVC were negatively and respectively correlated with the baseline and longitudinal change of HbA1c. Furthermore, compared to those without new-onset DM, participants with new-onset DM had a more pronounced decline of FVC over time.

Estimating the Cutoff Points of Time-Dependent Risk Factors by Using Joint Modeling of Longitudinal and Time-to-Event Data: A 14-Year Follow-up Study—Tehran Lipid and Glucose Study

2019 ◽  
Vol 31 (8) ◽  
pp. 728-736
Author(s):  
Nezhat Shakeri ◽  
Fereidoun Azizi
Keyword(s):  
Risk Factors ◽  
Cox Model ◽  
Joint Modeling ◽  
Time Dependent ◽  
Estimation Methods ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data ◽  
Cutoff Points

Diagnostic accuracy and optimal cutoff points of risk factors is one of the important issues in medical decisions. In order to reassess the cutoff points of markers, longitudinal and time-to-event data of elderly individuals were collected repeatedly through 3 follow-up stages in the Tehran Lipid and Glucose Study. Time-dependent area under the ROC (receiver operating characteristic) curves (AUCs) based on the joint modeling of longitudinal and time-to-event data technique were measured. AUCs were considered to evaluate the discriminative potential of the models. The joint model produced higher AUC values than the Cox model; therefore, accuracy was improved although it is computationally complicated. The results had some differences with the thresholds reported in guidelines due to specificity to the population and/or the means of estimation methods. The estimated cutoff points with regard to sex can be used as a guideline for the Iranian elderly population.

scholarly journals Impact of Model Choice When Studying the Relationship Between Blood Pressure Variability and Risk of Stroke Recurrence

Hypertension ◽  
2021 ◽  
Author(s):  
Hugues de Courson ◽  
Loïc Ferrer ◽  
Antoine Barbieri ◽  
Phillip J. Tully ◽  
Mark Woodward ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Blood Pressure Variability ◽  
Proportional Hazards ◽  
Cox Model ◽  
Positive Association ◽  
Vascular Risk Factor ◽  
Stroke Recurrence ◽  
The Impact ◽  
Over Time

Long-term blood pressure variability (BPV), an increasingly recognized vascular risk factor, is challenging to analyze. The objective was to assess the impact of BPV modeling on its estimated effect on the risk of stroke. We used data from a secondary stroke prevention trial, PROGRESS (Perindopril Protection Against Stroke Study), which included 6105 subjects. The median number of blood pressure (BP) measurements was 12 per patient and 727 patients experienced a first stroke recurrence over a mean follow-up of 4.3 years. Hazard ratios (HRs) of BPV were estimated from 6 proportional hazards models using different BPV modeling for comparison purposes. The 3 commonly used methods first derived SD of BP measures observed over a given period of follow-up and then used it as a fixed covariate in a Cox model. The 3 more advanced modeling accounted for changes in BP or BPV over time in a single-stage analysis. While the 3 commonly used methods produced contradictory results (for a 5 mmHg increase in BPV, HR=0.75 [95% CI, 0.68–0.82], HR=0.99 [0.91–1.08], HR=1.19 [1.10–1.30]), the 3 more advanced modeling resulted in a similar moderate positive association (HR=1.08 [95% CI, 0.99–1.17]), whether adjusted for BP at randomization or mean BP over the follow-up. The method used to assess BPV strongly affects its estimated effect on the risk of stroke, and should be chosen with caution. Further methodological developments are needed to account for the dynamics of both BP and BPV over time, to clarify the specific role of BPV.

scholarly journals Random survival forests for dynamic predictions of a time-to-event outcome using a longitudinal biomarker

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Kaci L Pickett ◽  
Krithika Suresh ◽  
Kristen R Campbell ◽  
Scott Davis ◽  
Elizabeth Juarez-Colunga
Keyword(s):  
Proportional Hazards ◽  
Prediction Models ◽  
Predictive Performance ◽  
Joint Modeling ◽  
Complex Relationship ◽  
Time To Event ◽  
Dynamic Prediction ◽  
Random Survival Forests ◽  
Survival Forests ◽  
Over Time

Abstract Background Risk prediction models for time-to-event outcomes play a vital role in personalized decision-making. A patient’s biomarker values, such as medical lab results, are often measured over time but traditional prediction models ignore their longitudinal nature, using only baseline information. Dynamic prediction incorporates longitudinal information to produce updated survival predictions during follow-up. Existing methods for dynamic prediction include joint modeling, which often suffers from computational complexity and poor performance under misspecification, and landmarking, which has a straightforward implementation but typically relies on a proportional hazards model. Random survival forests (RSF), a machine learning algorithm for time-to-event outcomes, can capture complex relationships between the predictors and survival without requiring prior specification and has been shown to have superior predictive performance. Methods We propose an alternative approach for dynamic prediction using random survival forests in a landmarking framework. With a simulation study, we compared the predictive performance of our proposed method with Cox landmarking and joint modeling in situations where the proportional hazards assumption does not hold and the longitudinal marker(s) have a complex relationship with the survival outcome. We illustrated the use of the RSF landmark approach in two clinical applications to assess the performance of various RSF model building decisions and to demonstrate its use in obtaining dynamic predictions. Results In simulation studies, RSF landmarking outperformed joint modeling and Cox landmarking when a complex relationship between the survival and longitudinal marker processes was present. It was also useful in application when there were several predictors for which the clinical relevance was unknown and multiple longitudinal biomarkers were present. Individualized dynamic predictions can be obtained from this method and the variable importance metric is useful for examining the changing predictive power of variables over time. In addition, RSF landmarking is easily implementable in standard software and using suggested specifications requires less computation time than joint modeling. Conclusions RSF landmarking is a nonparametric, machine learning alternative to current methods for obtaining dynamic predictions when there are complex or unknown relationships present. It requires little upfront decision-making and has comparable predictive performance and has preferable computational speed.

scholarly journals Joint Modeling of Longitudinal Markers and Time-to-Event Outcomes: An Application and Tutorial in Patients After Surgical Repair of Transposition of the Great Arteries

2021 ◽  
Author(s):  
Sara J. Baart ◽  
Roel L.F. van der Palen ◽  
Hein Putter ◽  
Roula Tsonaka ◽  
Nico A. Blom ◽  
...  
Keyword(s):  
Surgical Repair ◽  
Cox Model ◽  
Joint Modeling ◽  
Joint Model ◽  
Repeated Measurements ◽  
Time Dependent ◽  
Valve Regurgitation ◽  
Time To Event ◽  
Modeling Framework

Background: Most patients with congenital heart disease survive into adulthood; however, residual abnormalities remain and management of the patients is life-long and personalized. Patients with surgical repair of transposition of the great arteries, for example, face the risk to develop neoaortic valve regurgitation. Cardiologists update the prognosis of the patient intuitively with updated information of the cardiovascular status of the patient, for instance from echocardiographic imaging. Methods: Usually a time-dependent version of the Cox model is used to analyze repeated measurements with a time-to-event outcome. New statistical methods have been developed with multiple advantages, of which the most prominent one being the joint model for longitudinal and time-to-event outcome. In this tutorial, the joint modeling framework is introduced and applied to patients with transposition of the great arteries after surgery with a long-term follow-up, where repeated echocardiographic values of the neoaortic root are evaluated against the risk of neoaortic valve regurgitation. Results: The data are analyzed with the time-dependent Cox model as benchmark method, and the results are compared with a joint model, leading to different conclusions. The flexibility of the joint model is shown by adding the growth rate of the neoaortic root to the model and adding repeated values of body surface area to obtain a multimarker model. Lastly, it is demonstrated how the joint model can be used to obtain personalized dynamic predictions of the event. Conclusions: The joint model for longitudinal and time-to-event data is an attractive method to analyze data in follow-up studies with repeated measurements. Benefits of the method include using the estimated natural trajectory of the longitudinal outcome, great flexibility through multiple extensions, and dynamic individualized predictions.

Bedside assessment of the risk of non-compliance to medication is associated with mortality in elderly patients admitted for acute coronary syndromes

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
C Briet ◽  
C Lacote ◽  
C Peron ◽  
K Blanchart ◽  
A Lemaitre ◽  
...  
Keyword(s):  
Elderly Patients ◽  
Acute Coronary Syndromes ◽  
Cox Model ◽  
Fold Increase ◽  
Bedside Test ◽  
Tertiary Center ◽  
Coronary Syndromes ◽  
One Year ◽  
Grace Score

Abstract Background Elderly patients are at high risk of mortality in the setting of acute coronary syndromes (ACS). Purpose We investigated whether compliance assessed by Compliance Evaluation Test (CET) in elderly patients admitted for acute coronary syndromes was associated with higher risk of one-year mortality. Methods We used the data from a prospective, open, ongoing cohort of patients ≥75 years old admitted for ACS to a tertiary center. The CET is a validated 6 item test easily performed at bedside. Non-compliance is defined by ≥ “Yes” answers. We used a Cox model, un-adjusted and adjusted on predefined correlates of mortality (age, gender, and GRACE score) to assess the relationship between the risk of non-compliance and 1-year mortality. Results Two hundred fifty-five consecutive patients (age 83±5, female gender 59.6%, GRACE score 175±24) with CET assessment within 48 hours after admission and 1 year follow-up were included in the analysis. 225 (88%) were identified as compliant and 30 (12%) as non-compliant based on the CET. Thirthy-six deaths occurred at 1 year follow-up, 24 (10.6%) and 12 (30%) in compliant and non-compliant patients respectively. There was an almost 4-fold increase in the risk of one-year mortality in association with non-compliance (HR 4.16; 95% CI 2.03 to 8.5, p&lt;0.0001) and adj-HR 3.93; 95% CI 1.87 to 8.3, p=0.003), independent of other covariables. Conclusions In elderly patients admitted for ACS, the risk of non-compliance assessed by the simple bedside test is associated with a 4-fold increase in the risk of 1-year mortality independent of other correlates of mortality. Our results support specific measures to improve compliance in such patients. Survival based on compliance test Funding Acknowledgement Type of funding source: None

scholarly journals Dynamic prediction of overall survival: a retrospective analysis on 979 patients with Ewing sarcoma from the German registry

BMJ Open ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. e036376
Author(s):  
Chuchu Liu ◽  
Anja J Rueten-Budde ◽  
Andreas Ranft ◽  
Uta Dirksen ◽  
Hans Gelderblom ◽  
...  
Keyword(s):  
Prediction Model ◽  
Ewing Sarcoma ◽  
Prediction Models ◽  
Postoperative Radiotherapy ◽  
Time Dependent ◽  
Strong Impact ◽  
Dynamic Prediction ◽  
Dependent Variables ◽  
Over Time

ObjectivesThis study aimed at developing a dynamic prediction model for patients with Ewing sarcoma (ES) to provide predictions at different follow-up times. During follow-up, disease-related information becomes available, which has an impact on a patient’s prognosis. Many prediction models include predictors available at baseline and do not consider the evolution of disease over time.SettingIn the analysis, 979 patients with ES from the Gesellschaft für Pädiatrische Onkologie und Hämatologie registry, who underwent surgery and treatment between 1999 and 2009, were included.DesignA dynamic prediction model was developed to predict updated 5-year survival probabilities from different prediction time points during follow-up. Time-dependent variables, such as local recurrence (LR) and distant metastasis (DM), as well as covariates measured at baseline, were included in the model. The time effects of covariates were investigated by using interaction terms between each variable and time.ResultsDeveloping LR, DM in the lungs (DMp) or extrapulmonary DM (DMo) has a strong effect on the probability of surviving an additional 5 years with HRs and 95% CIs equal to 20.881 (14.365 to 30.353), 6.759 (4.465 to 10.230) and 17.532 (13.210 to 23.268), respectively. The effects of primary tumour location, postoperative radiotherapy (PORT), histological response and disease extent at diagnosis on survival were found to change over time. The HR of PORT versus no PORT at the time of surgery is equal to 0.774 (0.594 to 1.008). One year after surgery, the HR is equal to 1.091 (0.851 to 1.397).ConclusionsThe time-varying effects of several baseline variables, as well as the strong impact of time-dependent variables, show the importance of including updated information collected during follow-up in the prediction model to provide accurate predictions of survival.

scholarly journals Determinants of Longitudinal Change of Glycated Hemoglobin in a Large Non-Diabetic Population

2021 ◽  
Vol 11 (7) ◽  
pp. 648
Author(s):  
Ho-Ming Su ◽  
Wen-Hsien Lee ◽  
Ying-Chih Chen ◽  
Yi-Hsueh Liu ◽  
Jiun-Chi Huang ◽  
...  
Keyword(s):  
Heart Rate ◽  
Total Cholesterol ◽  
Glycated Hemoglobin ◽  
Fasting Glucose ◽  
Longitudinal Change ◽  
Diabetic Patients ◽  
Diabetic Population ◽  
Over Time ◽  
New Onset

Although many cross-section studies have assessed the determinants of glycated hemoglobin (HbA1c), there have been limited studies designed to evaluate the temporal correlates of HbA1c in non-diabetic patients. This study aimed to identify the major determinants of longitudinal change of HbA1c in non-diabetic patients. This study included subjects from the 104,451 participants enrolled between 2012 and 2018 in the Taiwan Biobank. We only included participants with complete data at baseline and follow-up (n = 27,209). Patients with diabetes at baseline or follow-up (n = 3983) were excluded. Finally, 23,226 participants without diabetes at baseline and follow-up were selected in this study. △Parameters was defined as the difference between the measurement baseline and follow-up. Multivariable linear regression analysis was used to identify the major determinants of HbA1c longitudinal change (△HbA1c). During a mean 3.8 year follow-up, after multivariable analysis, new-onset hypertension (coefficient β: 0.014, p < 0.001), high △heart rate (coefficient β: 0.020, p = 0.002), high △BMI (coefficient β: 0.171, p = 0.028), high △fasting glucose (coefficient β: 0.107, p < 0.001), low △creatinine (coefficient β: −0.042, p < 0.001), high △total cholesterol (coefficient β: 0.040, p < 0.001), high △hemoglobin (coefficient β: 0.062, p < 0.001), high △GPT (coefficient β: 0.041, p = 0.001), and low △albumin (coefficient β: −0.070, p < 0.001) were significantly associated with high △HbA1c. In non-diabetic population, strategies to decrease the development of new-onset hypertension, resting heart rate, body mass index, fasting glucose, total cholesterol, and GPT and increase serum albumin level might be helpful in slowing the longitudinal change of HbA1c. In addition, increased hemoglobin and decreased serum creatinine over time also had an impact on the HbA1c elevation over time in non-diabetic population.

Sign in / Sign up

Export Citation Format

Share Document